Medical image processing device and medical image processing method

ABSTRACT

With respect to projection data covering a plurality of sites, in order to create a medical image in which a uniform noise reduction effect is achieved for all sites within the projection data, an arithmetic device ( 5 ) determines one or more of proper subsets on the basis of scanning conditions and reconstruction conditions (step  2 ). Next, the arithmetic device ( 5 ) calculates a penalty-term weight for each proper subset on the basis of a detector output weight corresponding to a set element contained in the proper subset (step  3 ). Then, the arithmetic unit ( 5 ) performs an iterative approximation by using the penalty-term weight for each proper subset (step  4 ).

FIELD OF THE INVENTION

The present invention relates to a medical image processing device andthe like which perform a correction process and/or reconstructionprocess of projection data using the iterative approximation method thatincludes the weight in accordance with the output of a detector and thepenalty term in an evaluation function.

DESCRIPTION OF RELATED ART

An X-ray CT apparatus comprises an X-ray detector in which detectionelements are arrayed in the channel direction and the row direction. Ina scanning by an X-ray CT apparatus, by revolving an X-ray tube and anX-ray detector that are facing each other around an object to beexamined, projection data sets are collected in discrete positions ofthe X-ray tube in the revolving direction (which also are the positionsof the facing X-ray detector). In the following description, anacquisition unit of projection data in the respective X-ray tubepositions will be referred to as a “view”. After projection data iscollected, an arithmetic device acquires filtered projection data bysuperimposing a reconstruction filter in the channel direction of theprojection data for each view and each row, then performs backprojection with respect to the acquired filtered projection data whilebeing weighted in the view direction, so as to create a cross-sectionalimage as the distribution map of the X-ray attenuation coefficientinside of the object in a non-destructive manner. In the followingdescription, the method for creating a cross-sectional image fromprojection data will be referred to as the “image reconstructionmethod”, and an image obtained by the image reconstruction method willbe referred to as a CT image.

The image reconstruction method is roughly divided into an analyticalmethod and an iterative approximation method. The analytical methodanalytically solves a problem based on the projection cross-sectionaltheorem. The iterative approximation method models the observing systemwhich achieves the acquisition of projection data mathematically, andestimates the best image using the iterative method on the basis of themathematical model.

By comparison, the advantage of the analytical method is a predominantlysmaller amount of calculation since reconstruction images can bedirectly obtained from the projection data. On the other hand, theadvantage of the iterative method is that artifacts (cone beamartifacts, etc.) or quantum noise on an image to be generated in theanalytical method can be reduced since physical processes for achievingprojection data acquisition or statistical fluctuation included inprojection data can be considered respectively as a mathematical modelor a statistical model.

As the image reconstruction method in a multi-slice CT, the analyticalmethod such as the Feldkamp method or the improved version of Feldkampmethod have mainly been used on the ground of a small amount ofcalculation. However, with recent technical advancement of computers,practical use of the iterative approximation method has also beenconsidered. In particular, a technique for improving the image qualityby the iterative approximation method has been proposed.

Here, the iterative approximation method is roughly divided into thefollowing two kinds by the difference of estimated variables.

-   (1) The method for constructing an evaluation function by setting    the projection value of projection data as the variable.-   (2) The method for constructing an evaluation function by setting    the pixel value of image data as variables.

The method (1) is applied to processes such as correction of projectiondata to perform in prior to an image reconstruction process. In thefollowing description, the correction of projection data to which theiterative approximation method of (1) is applied will be referred to asan “iterative approximation projection data correcting process”.

The method of (2) is applied to an image reconstruction process. In thefollowing description, the image reconstruction process to which theiterative approximation method of (2) is referred to as an “iterativeapproximation reconstructing process”.

In the iterative approximation projection data correcting process, theassessment index of projection data is set in advance, and theprojection data is iteratively updated so that the evaluation valuewhich is the numerical conversion of an assessment index takes themaximum value or the minimum value. As for the evaluation index,inconsistency between interim image data which is to be updated and themeasured projection data or probabilistic plausibility is to be used. Asan example, the iterative approximation projection data correctingprocess has been proposed in Non-patent Document 1, which uses thepenalized weighted square error function which is expressed by thefollowing equation as the evaluation function.

$\begin{matrix}{\lbrack {{Equation}\mspace{14mu} 1} \rbrack \mspace{619mu}} & \; \\{{\Phi (p)} = {{\sum\limits_{i = 1}^{I}{d_{i}( {y_{i} - p_{i}} )}^{2}} + {\beta {\sum\limits_{i = 1}^{I}{\sum\limits_{k \in \kappa_{i}}{v_{ik}{\psi ( {p_{i} - p_{k}} )}}}}}}} & (1)\end{matrix}$

In the equation (1), 1, . . . , i, . . . , I are serial numbers foruniquely specifying combination of a channel of a detector, a row of thedetector and a view. P_(i) and y_(i) are the interim projection data andmeasured projection data to be specified by serial number irespectively. P={p₁, . . . p_(i), . . . p_(I)} indicates vectors of theinterim projection data. κ_(i) is a set of detection elements in thevicinity of the detection element specified by serial number i.

ν_(ik) is a constant which indicates the correlativity between thedetection element specified by serial number i and the detection elementspecified by serial number k. In Non-patent Document 1, ν_(ik) isempirically determined.

Ψ (p_(i)-p_(k)) is a potential function in which the contrast betweeninterim projection data p_(i) and interim projection data p_(k) is setas a variable. In Non-patent Document 1, a quadratic function is used asthe potential function.

d_(i) is the weighted coefficient to be weighted on the difference valuebetween the measured projection data y_(i) and the forward projectiondata. Since d_(i) in image processing of CT images is the value whichreflects detector output of the detection element specified by serialnumber i, d_(i) will be referred to as “detector output weight” in thefollowing description. The detector output is the value which isdependent on the number of detector photons.

β is an arbitrary constant.

In an iterative approximation reconstruction process, an evaluationindex of image data is set in advance, and the image data is iterativelyupdated so that the evaluation value which is numerical conversion ofthe assessment index takes the maximum value or the minimum value. Asfor the evaluation index, inconsistency between the forward projectiondata in which interim image data is converted into projection data in anupdating process and the measured projection data or probabilisticplausibility is to be used. As an example, the iterative approximationreconstruction process has been proposed in Non-patent Document 2, whichuses the penalized weighted square error function which is expressed bythe following equation as the evaluation function.

$\begin{matrix}{\lbrack {{Equation}\mspace{14mu} 2} \rbrack \mspace{619mu}} & \; \\{{\Phi (x)} = {{\sum\limits_{i = 1}^{I}{d_{i}( {y_{i} - {\sum\limits_{j = 1}^{J}{a_{ij}x_{j}}}} )}^{2}} + {\beta {\sum\limits_{j = 1}^{J}{\sum\limits_{k \in \chi_{j}}{w_{jk}{\psi ( {x_{j} - x_{k}} )}}}}}}} & (2)\end{matrix}$

In the equation (2), 1, . . . j, . . . , J are serial numbers foruniquely specifying the pixels in image data (two-dimensionalcoordinates). X_(j) is the pixel value in the pixel specified by serialnumber j. X={X₁, . . . X_(j), . . . , X_(j)} is a vector whichrepresents image data.

a_(ij) is a matrix element which associates image data with projectiondata. This matrix represents the characteristic of the scanning systemof an X-ray CT apparatus via a mathematical model, and is referred to asa “system matrix”.

X_(j) is a set of pixels that are in the vicinity of the pixel which isspecified by serial number j. W_(jk) is a constant indicating thecorrelativity between the pixel specified by serial number i and thepixel specified by serial number k. In general, w_(jk) is the inversenumber of the distance between the central position of the pixelspecified by serial number j and the central position of the pixelspecified by serial number k.

Ψ(X_(i)-X_(k)) is a potential function in which the contrast betweenpixel value X_(i) in the pixel to be specified by serial number j andpixel value X_(k) in the pixel value to be specified by serial number kis set as the variable. In Non-patent Document 2, a quadratic functionhas been proposed as the simplest potential function. Also, a methodwhich changes the form of potential function by an arbitrary parameteris proposed in Non-patent Document 3.

The other constants are the same as the equation (1).

In the updating equations which are developed from the equation (1) byvarious numerical analytical approaches, interim projection data p_(i)in the process of updating is smoothed by the penalty term of the secondorder according to the contrast from interim projection data p_(k) inthe vicinity while being subjected to the restriction of measuredprojection data y_(i) and detector output weight d_(i) by thefirst-order term of the equation (1). Therefore, arbitrary constant β ofequation (1) is the parameter for adjusting the degree in smoothing ofinterim projection data p_(i).

In the same manner, arbitrary constant β in the equation (2) is theparameter for adjusting the degree in smoothing of image data X_(j).

In Non-patent Document 2 and Non-patent Document 3, arbitrary constant βis a constant throughout the entire iterative approximationreconstructing process.

On the other hand, en approach for changing β is proposed in Non-patentDocument 4. When β remains as constant, the spatial resolution and noisereducing performance become inhomogeneous depending on the position ofthe pixel in a CT image. Thus in Non-patent Document 4, inhomogeneity ofspatial resolution and noise reducing performance is mitigated inaddition to the contrast of the pixels in the vicinity, by changing β inthe respective pixels.

PRIOR ART DOCUMENTS Non-Patent Documents

-   Non-patent Document 1: T. Li et. al., “Nonlinear Sinogram Smoothing    for Low-DoSe X-ray CT”, IEEE, TranS. Nucl. Sci., Vol. 51, No. 5, pp.    2505-2513, 2004-   Non-patent Document 2: K. Saueret. al., “A Local Update Strategy for    Iterative ReconStruCTion form ProjeCTionS”, IEEE. TranS. Signal.    Proc., Vol. 41, No. 2, pp. 534-548, 1993-   Non-patent Document 3: J. B. Thibault et. al., A three-dimenSional    StatiStical approach to improved image quality for multiSlicehelical    CT”, Med. PhyS., Vol. 34, No. 11, pp. 4526-4544, 2007-   Non-patent Document 4: H. R. Shi et. al., “Quadratic Regularization    DeSign for 2-D CT”, IEEE. TranS. Med. Imag., Vol. 28, No. 5, pp    645-656, 2009-   Non-patent Document 5: T. Goto et. al., “Weighted-Feldkamp algorithm    with SeleCTive narroweSt cone angle data for conebeam CT”, Proc. Of    The Eigth Meeting on Fully Three-dimenSional Image ReconStruCTion in    Radiology and Nuclear Medocine, pp. 189-192, 2005-   Non-patent Document 6: J. Wang et. al., “Penalized weighted    leaSt-SquareS approach to Sonogram noiSe reduCTion and image    reconStruCTion for low-doSe X-ray computed tomography”, IEEE. TranS.    Med. Imag., Vol. 25, No. 10, pp. 1272-1283, 2006

SUMMARY OF INVENTION Technical Problem

Upon applying an iterative approximation projection data correctingprocess or iterative approximation reconstructing process over pluralregions (a chest region and an abdominal region, etc.), it is preferablefor an observer of CT images that the quantum noise reducing effect andartifact reducing effect (hereinafter referred to as “noise reducingeffect” when the two are not distinguished) are performed evenly withoutdepending on a region.

However, when an iterative approximation projection data correctingprocess or iterative approximation reconstructing process are appliedover plural regions, magnitude of detector output weight d_(i) causesthe difference of noise reducing effect among plural regions in a CTimage. For example, a case is assumed here in which an iterativeapproximation method on the basis of the equation (1) or equation (2) isapplied to the range from a chest region to an abdominal region. Whencompared to a chest region which is less affected by X-ray attenuationdue to the existence of lungs (hollow organs) and has a large number ofelements i with a large value of detector output weight d_(i), anabdominal region having a small number of elements i with a large valueof detector output weight d_(i) is under less constraint of detectoroutput weight d_(i) in the equation (1) and equation (2). Therefore,when the same image processing is performed on the chest region and theabdominal region, the noise reducing effects will be different in eachregion, which leads to generation of undesired CT images for theobserver with varied noise reducing effects in different regions.

Also, variability of noise reducing effect among plural regions can bereduced when β is changed in the respective pixels as disclosed inNon-patent Document 4. However, by the homogenization of the magnitudeof detector output weight d_(i), the noise reducing effect in CT images,i.e. reduction of quantum noise and artifacts becomes insufficient.Further, a problem remains in the method disclosed in Non-patentDocument 4 that an immense amount of time is required for the process ofdetermining β in the respective pixels.

The objective of the present invention, considering the previouslydescribed problems, is to provide a medical image processing device,etc. capable of creating a medical image, with respect to projectiondata covering plural regions, in which the effect for reducing quantumnoise and artifacts is evenly performed in all regions included in theprojection data without drastically increasing the amount ofcalculation.

BRIEF SUMMARY OF THE INVENTION

A first invention for achieving the above-described objective is amedical image processing device which performs projection datacorrecting process and/or image reconstruction process using aniterative approximation method including the detector output weightwhich is a weight in accordance with an output of a detector and thepenalty term in an evaluation function, comprising:

a proper subject determining unit configured to determine, on the basisof scanning conditions and reconstruction conditions, one or more propersubjects from the universal set of which information for uniquelyspecifying combination of a channel of the detector, a row of thedetector and a view which is the acquisition unit of the projection datais set as a set element;

a penalty-term weight calculating unit configured to calculate for eachof the proper subsets the penalty-term weight which is the weightrelated to the penalty term on the basis of the detector output weightcorresponding to the set element included in the proper subset; and

an iterative approximation method executing unit configured to executethe iterative approximation method using the penalty-term weight foreach of the proper subsets.

A second invention is a medical image processing method which performsthe projection data correcting process and/or the image reconstructionprocess using an iterative approximation method including a detectoroutput weight which is a weight in accordance with an output of adetector and a penalty term in an evaluation function, including:

a step of determining, on the basis of scanning conditions andreconstruction conditions, one or more proper subsets from the universalset of which the information for uniquely specifying combination of achannel of the detector, a row of the detector and a view which is theacquisition unit of the projection data is set as a set element;

a step of calculating for each of the proper subsets a penalty-termweight which is a weight related to the penalty term on the basis of thedetector output weight corresponding to the set elements included in theproper subset; and a step of executing the iterative approximationmethod using the penalty-term weight for each of the proper subsets.

Effect of the Invention

In accordance with the present invention, with respect to projectiondata covering plural regions, it is possible to create medical images inwhich the reducing effect of quantum noise and artifacts is evenlyachieved in all regions included in the projection data withoutdrastically increasing the amount of calculation.

DESCRIPTION OF REFERENCE NUMERALS

FIG. 1 is an overall external view of an X-ray CT apparatus 1.

FIG. 2 is a configuration diagram of an X-ray CT apparatus.

FIG. 3 is a pattern diagram showing a cross-sectional image of shoulderregions in a phantom of a human body as well as the vertical directionview and the horizontal direction view with respect to thecross-sectional image.

FIG. 4 is a pattern diagram showing a coronal plane image in a phantomof a human body, as well as a view corresponding to a chest region and aview corresponding to an abdominal region in the coronal plane image.

FIG. 5 is a flowchart showing the flow of the overall medical imageprocessing.

FIG. 6 is a flowchart showing the calculation process of a firstpenalty-term weight.

FIG. 7 is a view showing an example of a histogram.

FIG. 8 is a flowchart showing the calculation process of a secondpenalty-term weight.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of the present invention will be described below in detailreferring to the attached drawings. First, the configuration of an X-rayCT apparatus will be described referring to FIG. 1 and FIG. 2.

As shown in FIG. 1, the X-ray CT apparatus 1 includes devices such as ascanner 2 in which an X-ray tube 11 and detector 12 are mounted, a bed 4on which an object 10 is placed, an arithmetic device 5 configured toexecute processing of data acquired from the detector 12, an inputdevice 6 such as a mouse, a trackball, a keyboard and a touch panel, anda display device 7 configured to display reconstruction images (CTimages).

An operator inputs scanning conditions or reconstruction conditions viathe input device 6. The scanning conditions include, for example thetranslation velocity of a bed, tube current, tube voltage, scanningrange (the range of the slice position), and scanning view number perrevolution. Also, the reconstruction conditions include, for example aregion of interest, reconstruction image size (the size of areconstruction image), and a reconstruction filtering function.

As shown in FIG. 2, the X-ray CT apparatus 1 is roughly divided into thescanner 2, an operation unit 3 and the bed 4.

The scanner 2 is configured by devices such as the X-ray tube 11 (X-raygeneration device), the detector 12, a collimator 13, a drive device 14,a central control unit 15, an X-ray control unit 16, a high-voltagegeneration device 17, a scanner control device 18, a bed control device19, a bed translation measurement device 20, a collimator control device21, a pre-amplifier 22, and an A/D converter 23.

The central control device 15 inputs scanning conditions orreconstruction conditions from the input device 6 in the operation unit3, and transmits the control signals necessary for scanning to thecollimator control device 21, the X-ray control device 16, the scannercontrol device 18, and the bed control device 19.

The collimator control device 21 controls the position of the collimator13 on the basis of the control signals.

When scanning is started by receiving a scanning start signal, the X-raycontrol device 16 controls the high-voltage generation device 17 on thebasis of the control signals. The high-voltage generation device 17applies a tube voltage and a tube current to the X-ray tube 11 (X-raygeneration device). In the X-ray tube 11, an energy electron inaccordance with the applied tube voltage is emitted from the cathode,and an X-ray of the energy in accordance with the electronic energy isirradiated to the object 10 by collision of the emitted electron againsta target (anode).

Also, the scanner control device 18 controls the drive device 14 basedon the control signals. The drive device 14 revolves a gantry unit inwhich the X-ray tube 11, the detector 12, the preamplifier 22, etc. aremounted around the object 10.

The bed control unit 19 controls the bed 4 based on the control signals.

The X-ray irradiated from the X-ray tube 11 is subjected to control ofthe irradiation region thereof by the collimator 13, absorbed(attenuated) in accordance with the X-ray attenuation coefficient ineach tissue of the object 10, passes through the object 10 to bedetected by the detector 12 which is placed at the position oppositefrom the X-ray tube 11. The detector 12 is formed by plural detectionelements placed in the two-dimensional direction (the channel directionand the row direction orthogonal thereto). The X-ray which receivedlight by the respective detection elements is converted into themeasured projection data. In other words, the X-ray detected by thedetector 12 is converted into a current, amplified by the pre-amplifier22, converted into digital data by the A/D converter 23, performed withLOG conversion, calibrated and input to the arithmetic device 5 asmeasured projection data.

At this time, since the X-ray tube 11 and the detector 12 which arefacing each other revolve around the object 10, the measured projectiondata can be understood as the discrete X-ray tube positions (facingdetector positions) in the rotational direction. The acquisition unit ofmeasured projection data in the respective X-ray tube positions is a“view”.

The arithmetic device 5 is formed by devices such as a reconstructionarithmetic device 31 and an image processing device 32. Also, theinput/output device 9 is formed by devices such as the input device 6(input unit), the display device 7 (display unit) and a storage device 8(storage unit).

The reconstruction arithmetic device 31 performs an image reconstructionprocess using measured projection data, and generates a reconstructionimage. The reconstruction arithmetic device 31 generates filteredprojection data by superimposing a reconstruction filter over themeasured projection data of each view, and creates a cross-sectionalimage in a non-destructive manner as a distribution chart of the X-rayattenuation coefficient inside of the object 10 by performing a backprojection process in which the view-direction weighting is implementedon the filtered projection data.

The reconstruction arithmetic device 31 stores the generatedreconstruction images in the storage device 8. Also, the reconstructionarithmetic device 31 displays the generated reconstruction image on thedisplay device 7 as a CT image. Or, the image processing device 32performs the image processing with respect to the reconstruction imagestored in the storage device 8, and displays the processed image on thedisplay device 7 as a CT image.

The X-ray CT apparatus 1 is roughly divided into a multi-slice CT whichuses the detector 12 in which detection elements are two-dimensionallyarrayed and a single-slice CT which uses the detector 12 in whichdetection elements are arrayed in a row, i.e. in one-dimensionaldirection (only in the channel direction). In a multi-slice CT, an X-raybeam is irradiated in concert with the detector 12 while being spread ina conical shape or pyramid shape from the X-ray tube 11 which is anX-ray source. In a single-slice CT, a fan-shaped X-ray beam isirradiated from the X-ray tube 11. Generally in scanning by the X-ray CTapparatus 1, irradiation of an X-ray is performed while the gantry unitrevolves around the object 10 which is placed on the bed 4 (excludingscanogram imaging).

The scanning mode in which the bed 4 is fixed during scanning and theX-ray tube 11 revolves around the object 10 in a form of circular orbitis referred to as axial scanning. In particular, the scanning mode whichrepeats scanning while the bed 4 is fixed and moving the bed 4 to thenext scanning position is referred to as, for example, step-and-shootscanning. Since axial scanning can be considered as step-and-shootscanning in which the bed 4 is moved to a scanning position only onetime, the both kinds of scanning will be referred to as step-and-shootscanning in the following description.

Also, the scanning mode in which the X-ray tube 11 revolves around theobject 10 in a helical manner while the bed 4 is translated continuallyis referred to as helical scanning.

In case of step-and-shoot scanning, the bed control device 20 holds thebed 4 still during scanning. Also in case of spiral scanning, the bedcontrol device 20 translates the bed 4 parallel to the body-axisdirection during scanning, in accordance with the bed-feeding velocitywhich is a scanning condition input via the input device 6.

The X-ray CT apparatus 1 is, for example a multi-slice CT. Also, thescanning method applied to the X-ray CT apparatus 1 is, for example therotate-rotate method (the third generation).

Next, detector output weight d_(i) in the equations (1) and (2) will bedescribed referring to FIG. 3 and FIG. 4.

FIG. 3 schematically shows a cross-sectional image 41 (CT image) of theshoulder regions in a phantom of a human body which is created by thereconstruction process of the X-ray CT apparatus 1, as well as thevertical-direction view (a) and the horizontal-direction view (b) of thecross-section image 41. In order to provide a simple explanation, onlytwo dimensions of a view and a channel 42 are indicated withoutconsidering the dimension of the rows in the detector 12. Thehorizontal-direction view (b) has a longer path length of an X-raythrough a human body compared to the vertical-direction view (a), andhas more channels 42 which indicate a small detector output value. Inother words, a large amount of noise is included in the detector outputin the horizontal-direction view (b). For this reason, streaky artifactsare generated mainly in the horizontal direction in a CT imagereconstructed by the image reconstruction method of the analyticalapproach.

Since detector output weight d_(i) of the equations (1) and (2) is thevalue in accordance with the detector output, detector output weightd_(i) corresponding to the horizontal-direction view (b) is smaller thandetector output weight d_(i) corresponding to the vertical-directionview (a). In other words, the horizontal-direction view (b) relativelyreceives less restriction by the first-order term in the equations (1)and (2) compared to the vertical-direction view (a), thus receives moresmoothing effect by the second-order term (penalty term). Therefore, inthe iterative approximation method using the evaluation function such asthe equations (1) and (2), the noise included in the detector output canbe averaged and streaky artifacts generated in CT images can beeffectively reduced. Also by the same principle, the iterativeapproximation method can effectively reduce quantum noises generated inCT images.

It is preferable that the quantum noise reducing effect and streakyartifact reducing effect (=noise reducing effect) in CT images areevenly provided without depending regions. However, when both iterativeapproximation processes are applied over plural regions, the differenceof degrees in detector output weights d_(i) causes the difference ofnoise reducing effects among plural regions in CT images.

FIG. 4 schematically shows, with respect to a chest region and anabdominal region of a phantom of a human body, a coronal plane image 43(CT image) created by the X-ray CT apparatus 1 using the reconstructionprocess, as well as a view range (a) corresponding to the chest regionand a view range (b) corresponding the abdominal region in the coronalplane image 43.

In order to provide a simple explanation, only two dimensions of a viewand a row 44 are indicated here without considering the dimension of thechannels in the detector 12. 45 indicates the scan direction, i.e. thedirection which is directly opposite to the direction for translatingthe bed 4.

As shown in FIG. 4, when the range from the chest region to theabdominal region is imaged by the X-ray CT apparatus 1, the averagevalue of detector outputs in the chest region is greater than theaverage value of the detector outputs in the abdominal region. This isbecause the chest region includes lungs (hollow regions) and theattenuation amount of an X-ray is relatively smaller compared to en areasuch as an abdominal region.

In other words, when the same iterative approximation method is used inan area from a chest region to an abdominal region without depending ona region using the evaluation function such as the equations (1) and(2), the abdominal region provided mostly with detector output weightd_(i) having a small value receives relatively small restriction of theequations (1) and (2) compared to the chest region provided mostly withdetector output weight d_(i) having a large value. Therefore, in theiterative approximation method using the evaluation functions such asthe equations (1) and (2), the reducing effect of streaky artifacts orquantum noises generated in CT images (noise reducing effect) variesdepending on a region.

In the medical image processing device of the present invention, suchvariability of noise reducing effects among plural regions is equalized.

Each step in the medical image processing device of the presentinvention will be executed after measured projection data is collected.Therefore, the medical image processing device of the present inventioncan be the arithmetic device 5 included in the X-ray CT apparatus 1 or ageneral-purpose computer which is not included in the X-ray CTapparatus. Further, the X-ray CT apparatus 1 and the medical imageprocessing device do not necessarily have to be connected via network.For the avoidance of confusion, the arithmetic device will be describedas the medical image processing device of the present invention.

In the same manner, an input unit, a display unit and a storage unitcomprised by the medical image processing device of the presentinvention may be an input device 6, a display device 7 and a storagedevice 8 included in the X-ray CT apparatus 1, as well as a devicecomprised by a general-purpose computer which is not included in theX-ray apparatus CT 1 or an external device. For the avoidance ofconfusion, the input device 6, the display device 7 and the storagedevice 8 will be described below as an input unit, a display unit and astorage unit comprised by the medical image processing device of thepresent invention.

Also, while the measured projection data is collected in a fan-beamsystem, it will be assumed that the data is converted into aparallel-beam system in the following description.

Embodiment 1

Embodiment 1 will be described referring to FIG. 5˜FIG. 8. In Embodiment1, a case in which the present invention is applied to the iterativeapproximation projection data correcting method will be described. Thearithmetic device 5 carries out in Embodiment 1 the projection datacorrecting process using the iterative approximation method in which thedetector output weight (the weight in accordance with the output of thedetector 12) and the penalty term are included in the evaluationfunction.

As shown in FIG. 5, an operator inputs scanning conditions andreconstruction conditions, as well as a desired calculation time anddesired noise attenuation rate of the iterative approximation process(step 1).

As for the scanning conditions, the arithmetic device 5 mayautomatically set the values stored in the storage device 8 along withthe projection data. For example, the arithmetic device 5 may displaythe values of the scanning condition stored in the storage device 8 onthe display device 7, so that all an operator needs to do is to confirmthe displayed values.

As for the reconstruction condition, a desired calculation time ordesired noise attenuation rate of the iterative approximation process,the arithmetic device 5 may also display the default value or options onthe display 7 to support an operator in performing input operation.

For example, the arithmetic device 5 may make the display unit 7 displaythe options such as “high speed”, “middle speed” and “low speed” as thesupport for inputting desired calculation times. In this case, thearithmetic device 5 stores the calculation time for each option in thestorage device 8. Then the arithmetic device 5 sets desired calculationtimes in accordance with the option selected by the operator.

The arithmetic device 5 may also display the options such as “high imagequality”, “middle image quality” and “low image quality” on the displayunit 7 for supporting the input operation of a desired noise attenuationrate. In this case, the arithmetic device 5 stores in advance the noiseattenuation rate for each option in the storage device 8. Then thearithmetic device 5 sets a desired noise attenuation rate in accordancewith the option selected by the operator.

Next, the arithmetic device 5 determines one or more proper subsets {S₁,. . . , S_(m), . . . , S_(M)} on the basis of the scanning condition andreconstruction condition that are input in step 1 (step 2).

Universal set Ω is provided with serial numbers {1, . . . i, . . . , I}for uniquely identifying combination of a channel of the detector 12, arow of the detector 12 and a view as a set element. More specifically,when the number of channels in detector 12 is C, the number of rows indetector 12 is R and the number of views in the entire scanning is V,Ω={1, 2, 3, . . . , . . . , C×R×V−2, C×R×V−1, C×R×V}.

Here, S_(m) being the proper subset of Ω means that S_(m) ⊂ Ω (S_(m) isthe subset of Ω) and also that S_(m)≠Ω. Further, it will be assumed,with respect to the set (S_(i),S_(j)) of an arbitrary proper subset,that S_(i)∩S_(j)=φ in the following description.

First, a case of step-and-shoot scanning will be described. Instep-and-shoot scanning, it is assumed that the same region is beingimaged while the position of the bed 4 is fixed. Then the arithmeticdevice 5 determines the set elements of the respective proper subsets sothat the projection data collected while the position of the bed 4 isfixed belongs to the same proper subset.

In case of step-and-shoot scanning, the number of partitions inuniversal set Ω i.e., the number M in proper subset S_(m) is the same asthe number of times that the position of bed 4 is moved.

Here, the number of views to be imaged in the respective positions ofthe bed 4 is set as V1, . . . , V_(m), . . . , V_(M). In this case, thenumber of views in an entire scanning is: V=V₁+ . . . +V_(m)+ . . .V_(m). Also, the number of set elements in each proper subset S_(m) isthe number V_(m)×C×R which is the multiplication of the number of viewsV_(m) to be imaged in the respective positions of bed 4, the number ofchannels C of the detector 12 and the number of rows R of the detector12.

Therefore, in each proper subset {S₁, . . . , S_(m), . . . , S_(M)},S₁={1, 2, . . . , V₁×C×R}, S₂={V₁×C×R+1, . . . , V₁×C×R+V₂×C×R}, . . . ,S_(m)={(V₁+V₂+ . . . +V_(m-1))×C×R+1, . . . , (V₁+V₂+ . . .+V_(m)−1+V_(m))×C×R}, . . . , S_(M)={(V₁+V₂+ . . . +V_(M-1))×C×R+1, . .. , (V₁+V₂+ . . . +V_(M-1)+V_(M))×C×R}.

For example, in each position of the bed 9, a view for one revolution(360°) is constantly imaged, and the number of views per revolution(=360°) is set as V₃₆₀. In this case, V₁= . . . =V_(m)= . . .=V_(M)=V₃₆₀, the number of set elements in each proper subsetS_(m)=V₃₆₀×C×R, and the number of set elements in the entire propersubset S_(m) becomes the same.

Next, a case of helical scanning will be described. In case of helicalscanning, the arithmetic device 5 determines the number of set elementsincluded in proper subset S_(m) based on the number of views defined asthe calculation unit of the back projection process in the iterativeapproximation method, i.e. the number of back projection views. This isbecause the arithmetic device 5 needs to determine each proper subsetS_(m) for acquiring the projection data necessary for creating a pieceof cross-sectional image as a set element.

For example, in the case that an iterative approximation process isimplemented by the method disclosed in Non-patent Document 5, the numberof back projection views equals the value in which phase width tw of theback projection (referred to as “back projection phase width tw”) ismultiplied by the number of scanning views V_(d) per revolution which isset as the scanning condition.

In back projection phase width tw, the value which indicates the phasefor one revolution (=360°) is 1, the value which indicates the phase forhalf a revolution (=180°) is ½, the value which indicates the phase fortwo revolutions (˜720°) is 2, and so on.

Here, back projection phase width tw is an arbitrary parameter, which isempirically determined in Non-patent Document 5 in accordance with thebed-feeding velocity and the size of a reconstructed image.

Thus it is preferable that the arithmetic device 5 stores backprojection phase width twin the storage device 8 in advance for eachbed-feeding velocity and the reconstructed image size. Then thearithmetic device 5 obtains from the storage device 8 a single backprojection phase width tw corresponding to the bed-feeding velocity setas a scanning condition and the reconstructed image size set as areconstruction condition. Next, the arithmetic device 5 sets the valuein which the number of scanning views V_(d) per revolution which is setas the scanning condition is multiplied by back projection phase widthtw obtained from the storage device 8 as the number of back projectionviews.

In a case in which an operator puts a high priority on the calculationvelocity, the proper subset may also be determined by replacing thenumber of back projection views as half a revolution (the minimum unitof back projection). Meanwhile, the value of the reconstructioncondition is to be applied to the back projection process withoutdepending on the replacement.

In case of helical scan, the number of partitions in universal set Ω,i.e. the number M of proper subsets S_(m) is the quotient in which thenumber of views V in an entire scanning is divided by the number of backprojection views tw×V_(d). When the number of views V in an entirescanning is aliquant by the number of back projection views tw×V_(d),the remaining views are to be included in the last proper subset S_(M).

The number of set elements in all proper subsets S_(m) is the same,which is the number tw×V_(d)×C×R which is the multiplication of backprojection phase width tw, the number of scanning views V_(d) perrevolution, the number of channels C and the number of rows R.

Therefore, in each proper subset {S₁, . . . , S_(m), . . . , S_(M)},S₁={1, 2, . . . , tw×V_(d)×C×R₁}, S₂={tw×V_(d)×C×R+1, . . . ,2×tw×V_(d)×C×R}, . . . , S_(m)={(m−1)×tw×V_(d)×C×R+1, . . . ,m×tw×V_(d)×C×R}, . . . , S_(M)={(M−1)×tw×V_(d)×C×R+1, . . . ,M×tw×V_(d)×C×R}.

The description will be returned to the explanation of FIG. 5. Next,arithmetic 5 calculates the weight related to a penalty term thepenalty-term weight) {β₁, . . . , β_(m), . . . , β_(M)} for each propersubset S_(m) determined in step 2 on the basis of detector output weightd_(i) corresponding to the set elements included in proper subset S_(m)(step 3). The detail of the penalty-term weight calculation process willbe described below referring to FIG. 6˜FIG. 8.

Next, the arithmetic device 5 executes the iterative approximationmethod with respect to iεS_(m) using penalty-term weight β_(m) for eachproper subset S_(m) (step 4). In other words, the arithmetic device 5executes the iterative approximation projection data correcting processwhile changing penalty-term weight β_(m) for each region. The followingequation is an example of an evaluation function.

$\begin{matrix}{\lbrack {{Equation}\mspace{14mu} 3} \rbrack \mspace{619mu}} & \; \\{{\Phi (x)} = {{\sum\limits_{i = 1}^{I}{d_{i}( {y_{i} - p_{i}} )}^{2}} + {\sum\limits_{m = 1}^{M}{\beta_{m}{\sum\limits_{i \in S_{m}}{\sum\limits_{k \in \kappa_{i}}{v_{ik}{\psi ( {p_{i} - p_{k}} )}}}}}}}} & (3)\end{matrix}$

Here, since the numerical analytical approach of the present inventionis to change the penalty-term weight in an evaluation function for eachregion, the present invention can be applied without depending on thenumerical analytical method to be used for optimization as long as theevaluation function includes a detector output weight and a penaltyterm.

The deviation of an updating equation in the iterative approximationmethod from the equation (3) can be performed by a commonly-knownmethod. For example, an updating equation can be deviated by using theGauss-Seidel method as disclosed in Non-patent Document 6.

The arithmetic device 5 deviates an updating equation in the iterativeapproximation method from the equation (3), substitutes calculateddetector output weight d_(i) and penalty-term weight for each propersubset S_(m) into the deviated updating equation, and performs theprojection data correcting process using the iterative approximationmethod.

In the following description, two kinds of penalty-term weightcalculation processes will be described in detail referring to FIG.6˜FIG. 8. The first penalty-term calculation process will be describedreferring to FIG. 6 and FIG. 7. The second penalty-term calculationprocess will be described referring to FIG. 8.

An operator can arbitrarily select either the process considering adesired calculation time or estimation accuracy of the penalty-termweight.

First, the first penalty-term weight calculation process will bedescribed.

In the first penalty-term weight calculation process, the arithmeticdevice 5 previously stores, for each noise reducing rate, in the storagedevice 8 the penalty-term weight value of which the noise reducing ratebeing comparable to that of the reference phantom is achieved(hereinafter referred to as “penalty-term weight reference value”).

For example, the arithmetic device 5 stores in the storage device 8 thenoise reducing rate table in which penalty-term weight reference valuesβ*₁₀, β*₂₀, β*₃₀, . . . are registered with respect to noise reducingrates 10%, 20%, 30%, . . . in advance. Penalty-term weight referencevalues β*₁₀, β*₂₀, β*₃₀, . . . are set as the values acquired byactually scanning a reference phantom which is an orbicular waterphantom or a simulation of a human body and performing an imagereconstruction process.

Then the arithmetic device 5 acquires the penalty-term weight referencevalue corresponding to the input noise reducing rate from the storagedevice 8. Next, the arithmetic device 5 calculates the representativevalue for each proper subset S_(m) on the basis of detector outputweight d_(i) corresponding to the set elements included in each propersubset S_(m). Next, the arithmetic device 5 sets the value which is themultiplication of the representative value for each proper subset S_(m)and the penalty-term weight reference value acquired from the storagedevice 8 as penalty-term weight β_(m) for each proper subset S_(m).

In particular, it is preferable that the arithmetic device 5 sets, foreach proper subset S_(m), anyone of three values that are the averagevalue and the central value of detector output weight d_(i)corresponding to the set elements included in proper subset S_(m) aswell as the class for segmenting an entire histogram categorized bydetector output weight d_(i) by a predetermined proportion, as therepresentative value for each proper subset S_(m).

The flowchart in FIG. 6 shows a case in which the class for segmentingan entire histogram categorized by detector output weight d_(i) iscalculated as the representative value.

Also, while the calculation process of penalty-term weight β_(m) isindicated with respect to proper subset S_(m) in the flowchart of FIG.6, it is the same in the other proper subsets.

As shown in FIG. 6, the arithmetic device 5 selects the table valuecorresponding to a desired noise reducing rate input by an operator fromthe noise reducing rate table stored in the storage device 8 (step 11).

Next, the arithmetic device 5 creates subset T_(m) of proper subsetS_(m) from the set elements included in proper subset S_(m) inaccordance with a desired calculation time input by the operator (step12).

The arithmetic device 5 creates subset T_(m) by thinning the setelements included in S_(m) on the basis of a previously defined thinningrule.

As for the thinning rule, for example, the views can be thinnedalternately. By doing so, the image quality of the ultimately createdcross-sectional image will be less likely to be affected. For example,when the views are obtained for every 1° in the revolution direction,the number of scanning views V_(d) per revolution becomes 360. Byalternately thinning 360 views, 180 views remain and the number of setelements in subset T_(m) becomes a half of the number of set elements inproper subset S_(m).

Also for example, thinning the far left channel and the far rightchannel in each view can be considered as a thinning rule. This isbecause the far left channel and the far right channel in each viewoften receive an X-ray which does not pass through an object and theimage quality of an ultimately created cross-sectional image is lesslikely to be affected. For example, with respect to the number ofchannels C, by thinning 10% of data from the far left channel and 10% ofdata from the far right channel, the number of set elements in subsetT_(m) becomes 4/5 of the set elements in proper subset S_(m).

The above-described different thinning rules can also be combined.

In any thinning rule (or combination of plural thinning rules), it ispossible to adjust the amount of thinning in accordance with a desiredcalculation time which is input by an operator. In this manner,calculation time can be adjusted according to the intention of anoperator by creating subset T_(m) of proper subsets S_(m) and performinga process to be described below on subset T_(m).

Next, the arithmetic device 5 constructs a histogram categorized bydetector output weight d_(i) corresponding to the set elements of subsetT_(m) created in step 12 (step 13). Here, any known method can be usedas the method for calculating detector output weight d_(i). For example,as disclosed in Non-patent Document 6, the arithmetic device 5 may alsocalculate detector output weight d_(i) by subjecting projection data tologarithmic inverse conversion then multiplying the converted data byair data.

FIG. 7 shows an example of a histogram. The histogram shown in FIG. 7has the horizontal axis (class) which indicates detector output weightd_(i) and the vertical axis (frequency) which indicates the number ofset elements corresponding to detector output weight d_(i) equal to thevalue of each class.

A histogram shows different distributions depending on the size orregion of an object, which is an index to represent the characteristicsof the regions in the object.

The histogram of a shoulder region 51 has its maximum peak where thevalue of the class is near 0. Also, the overall values of detectoroutput weight d_(i) are low in the histogram of the shoulder region 51.This is because the shoulder region 51 includes many bone regions inwhich a large amount of X-ray is reduced.

Also, the histogram of a chest region 52 and an abdominal region 53 havetheir maximum peaks where the value of the class is near 1. When thehistograms of the chest region 52 and the abdominal region 53 arecompared, the frequency of the classes 1˜3 is: the histogram of thechest region 52 >the histogram of the abdominal region 53, and thefrequency of the classes above 5 is: the histogram of the chest region52 <the histogram of the abdominal region 53. This is because the chestregion 52 includes lungs of which the attenuation amount of X-rays issmall.

The histogram shown in FIG. 7 also represents the characteristic of thesize of an object as well, since the class at which the maximum peakexists changes depending on the size of the object.

The explanation returns to FIG. 6. Next, the arithmetic device 5calculates the area of each histogram shown in FIG. 7, and specifies theclass for segmenting the area of the histogram into a predeterminedproportion as the representative value (step 14). In addition, therepresentative value may also be the central value or the average valueof detector output weight d_(i) included in iε=S_(m) as previouslydescribed. An operator can arbitrarily select the representative value.

Next, the arithmetic device 5 calculates the penalty-term weight β_(m)of proper subset S_(m) by multiplying the table value selected in step11 by the class (representative value) specified in step 14 (step 15).

The calculation process of the first penalty-term weight does notrequire complicated calculations, thus penalty-term weight β_(m) ofproper subset S_(m) can be calculated without drastically increasing theamount of calculation.

Next, the calculation process of the second penalty-term weight will bedescribed. The calculation process of the second penalty-term weight isbased on the knowledge as follows.

-   (1) In a case in which the detector output weight is set as a    constant and the iterative approximation method is executed by an    arbitrary penalty-term weight, it is empirically evident that    approximately the same noise reducing rate can be obtained without    depending on an object and a region.-   (2) There is a high correlation between the correction amount of a    projection value (pixel value) by the iterative approximation method    and the noise reducing rate.

The above-described knowledge can be applied to both of the iterativeapproximation projection data correcting process and the iterativeapproximation reconstructing process.

Using the above-described knowledge, in the second penalty-term weightcalculation method, the arithmetic device 5 substitutes detector outputweight d₁ corresponding to the set elements included in proper subsetS_(m) and the projection data into the correction amount calculatingfunction which calculates the correction amount of the projection datain a case in which the iterative approximation method is executed. Here,penalty-term weight β_(m) for each proper subset S_(m) is included inthe correction amount calculating function as a variable. Then thearithmetic device 5 determines penalty-term weight β_(m) for each propersubset S_(m) so as to minimize the error in the value of the correctionamount calculating function (=summation of the correction amount ofprojection data) and a predetermined correction amount reference value(=the reference value of the summation of the correction amount inprojection data).

The determination process of a predetermined correction amount referencevalue is as follows. The arithmetic device 5, as in the calculationprocess of the first penalty-term weight, stores a noise reducing ratetable in the storage device 8, and obtains the penalty-term weightreference value corresponding to the input noise reducing rate from thestorage device 8. Then the arithmetic device 5 sets the detector outputweight as a constant, calculates the correction amount of projectiondata in a case wherein the iterative approximation method is executedusing the penalty-term weight reference value obtained from the storagedevice 8, and sets the calculated correction amount as a predeterminedcorrection amount reference value.

The second penalty-term weight calculating process will be describedbelow in detail referring to FIG. 8. While the calculation process ofpenalty-term weight β_(m) is shown in the flowchart of FIG. 8 withrespect to proper subset S_(m), the same process can also be applied tothe other proper subsets.

As shown in FIG. 8, the arithmetic device 5 selects the table valuecorresponding to the noise reducing rate input by an operator from thenoise reducing rate table stored in the storage device 8 (step 21). Inaddition, the noise reducing table to be used for calculating the firstpenalty-term weight and the noise reducing rate table to be used forcalculating the second penalty-term weight have different stored tablevalues due to the difference in the processing content.

Next, the arithmetic device 5 creates subset T_(m) of proper subsetS_(m) from the set elements included in proper subset S_(m) inaccordance with a desired calculation time input by the operator (step22). The creation process of subset T_(m) is the same as in step 12.

Next, the arithmetic device 5 estimates the projection value afterapplying the iterative approximation projection data correcting processby setting detector output weight d_(i) as an arbitrary constant, usingthe table value selected in step 21 and the projection datacorresponding to the set elements of subset T_(m) created in step 22.Here, since it is difficult to analytically calculate a projection valueafter being applied from the viewpoint of the calculation amount and thememory required for the calculation, an approximation value is used as asubstitute. By assuming that the focused projection value is calculatedonly from the adjacent channels, rows and views, the approximation valuecan be easily calculated. The approximation value may also be calculatedby replacing the back matrix necessary for calculation of the projectionvalue after iteration into an approximate matrix using a known method.Then the arithmetic device 5 calculates the error of the approximationvalue and the projection value in the iterative approximation projectiondata correcting process before application, and sets the summation ofthe errors as the correction amount reference value (step 23). Acommonly-known index such as an absolute error or mean square error canbe used for the error calculation process here.

Next, the arithmetic device 5 substitutes the correction amountcalculating function having a variable of penalty-term weight β_(m) withdetector output weight d_(i) and the projection data corresponding tothe set elements of subset T_(m) created in step 23. Then the arithmeticdevice 5 determines the penalty-term weight so that the value of thecorrection amount calculating function equals to the correction amountreference value calculated in step 23 (step 24). Here, a commonly-knownnumerical analytical approach (for example, the bisection method) can beapplied to minimize the error in the correction amount calculatingfunction and the correction amount reference value.

The calculation process of the second penalty-term weight is based onthe previously described knowledge, thus penalty-term weight β_(m) ofproper subset S_(m) can be calculated with high accuracy.

As described above, in accordance with Embodiment 1, it is possible toachieve the noise reducing effect in CT images as is conventionally donein the iterative approximation projection data correcting process bysetting penalty-term weight β_(m) to be used in the iterativeapproximation data correcting process as a constant in proper subsetS_(m).

Also, the variability of the noise reducing effect among plural regionscan be suppressed by calculating penalty-term weight β_(m) for eachproper subset S_(m) on the basis of the detector output weight d_(i) andthe projection value to which the information of an object is reflected.

Embodiment 2

Embodiment 2 will be described below. In Embodiment 2, a case in whichthe present invention is applied to the iterative approximationreconstructing process will be described. That is, the arithmetic device5 executes an image reconstruction process in Embodiment 2 using theiterative approximation method in which the detector output weight andthe penalty-term weight are included in an evaluation function.

The difference from Embodiment 1 is only that the equation (3) inEmbodiment 1 changes to the following equation.

Embodiment 4

$\begin{matrix}{{\Phi (x)} = {{\sum\limits_{m = 1}^{M}{\frac{1}{\beta_{m}}{\sum\limits_{i \in S_{m}}{d_{i}( {y_{i} - {\sum\limits_{j = 1}^{J}{a_{ij}x_{j}}}} )}^{2}}}} + {\sum\limits_{j = 1}^{J}{\sum\limits_{k \in \chi_{j}}{w_{jk}{\psi ( {x_{j} - x_{k}} )}}}}}} & (4)\end{matrix}$

As is in the equation (3), the updating equation in the iterativeapproximation method can be developed from the equation (4). Thearithmetic device 5 substitutes the detector output weight d_(i) whichis calculated in the same manner as in Embodiment 1 and penalty-termweight β_(m) for each proper subset S_(m) into the developed updatingequation, and executes the image reconstruction process using theiterative approximation method.

As described above, in Embodiment 2, the noise reducing effect in CTimages can be achieved as is conventionally done in the iterativeapproximation reconstructing process by setting penalty-term weightβ_(m) to be used in the iterative approximation reconstructing processas a constant in proper subset S_(m) as in Embodiment 1.

Also, the variability of noise reducing effect among regions can besuppressed by calculating penalty-term weight β_(m) for each proper setS_(m) on the basis of detector output weight d_(i) and the projectionvalue to which the information of an object is reflected as inEmbodiment 1.

Embodiment 3

Embodiment 3 will now be described. In Embodiment 3, a case to which thepresent invention is applied to both the iterative approximationprojection data correcting process and the iterative approximationreconstructing process will be described. That is, in Embodiment 3, thearithmetic device 5 executes the projection data correcting process andthe image reconstruction process using the iterative approximationmethod in which a detector output weight and a penalty term are includedin an evaluation function.

Embodiment 3 is the combination of Embodiment 1 and Embodiment 2. Morespecifically, the arithmetic device 5 develops an updating equation inthe iterative approximation method from the equation (3), substitutesdetector output weight d_(i) and penalty-term weight d_(i) for eachproper subset S_(m) which are calculated as in Embodiment 1 into thedeveloped updating equation, and performs the correcting process of theprojection data using the iterative approximation method as inEmbodiment 1. The arithmetic device 5 also develops an updating equationin the iterative approximation method from the equation (4), substitutesdetector output weight d_(i) and penalty-term weight β_(m) for eachproper subset S_(m) calculated as in Embodiment 1 into the developedupdating equation, and performs the image reconstruction process usingthe iterative approximation method, as in Embodiment 2.

As described above, in Embodiment 3, the noise reducing effect in CTimages can be achieved as is conventionally done in the iterativeapproximation projection data correcting process and the iterativeapproximation reconstructing process by setting penalty-term weightβ_(m) to be used in the iterative approximation projection datacorrecting process and the iterative approximation reconstructingprocess as a constant in proper subset S_(m).

Also, the variability of noise reducing effect among regions can besuppressed by calculating penalty-term weight β_(m) for each proper setS_(m) on the basis of detector output weight d_(i) and the projectionvalue to which the information of an object reflected.

The preferable embodiments of the medical image processing device, etc.related to the present invention have been described referring to theattached drawings. However, the present invention is not limited tothese embodiments. It is obvious that persons skilled in the art canmake various kinds of alterations or modifications within the scope ofthe technical idea disclosed in this application, and it isunderstandable that they belong to the technical scope of the presentinvention.

DESCRIPTION OF REFERENCE NUMERALS

-   -   1 X-ray CT apparatus    -   4 bed    -   5 arithmetic device    -   6 input device    -   7 display device    -   8 storage device    -   11 X-ray tube    -   12 detector    -   41 cross-sectional image    -   42 channel    -   43 coronal cross-sectional image    -   44 row    -   45 scan direction

1. A medical image processing device configured to execute a projectiondata correcting process and/or an image reconstruction process using aniterative approximation method in which a detector output weight whichis a weight in accordance with an output of a detector and apenalty-term are included in an evaluation function, comprising: aproper subset determining unit configured to determine one or moreproper subsets, on the basis of scanning conditions and reconstructionconditions, from a universal set of which the information for uniquelyspecifying combination of a channel of the detector, a row of thedetector and a view which is the acquisition unit of the projection datais a set element; a penalty-term weight calculating unit configured tocalculate, for each of the proper subsets, a penalty-term weight whichis a weight related to the penalty term on the basis of the detectoroutput weight corresponding to the set element included in the propersubset; and an iterative approximation method executing unit configuredto execute the iterative approximation method using the penalty-termweight for each of the proper subsets.
 2. The medical image processingdevice according to claim 1, wherein the proper subset determining unitdetermines, in case of helical scan, the number of the set elementsincluded in the proper subset based on the number of back projectionviews which is the number of the views defined as an accounting unit fora back projection process in the iterative approximation method.
 3. Themedical image processing device according to claim 2, wherein the propersubset determining unit: stores in advance in a storage device the backprojection phase width which is a phase width of a back projectionprocess in the iterative approximation method for each bed translationvelocity and each size of the reconstruction image; acquires from thestorage device the back projection phase width corresponding to bedtranslation velocity set as the scanning condition and size of thereconstruction image set as the reconstruction condition; and sets avalue in which the number of scanning views per revolution set as thescanning condition is multiplied by the back projection phase widthacquired from the storage device as the number of back projection views.4. The medical image processing device according to claim 1, wherein thepenalty-term weight calculating unit: stores in advance in the storageunit, for each noise reducing rate, a penalty-term weight referencevalue which is the penalty-term weight by which the noise reductioncomparable to that of the noise reducing rate is achieved with respectto a reference phantom; acquires the penalty-term weight reference valuecorresponding to the input noise reducing rate from the storage device;calculates, for each of the proper subsets, a representative value onthe basis of the detector output weight corresponding to the setelements included in the proper subset; and sets the value in which therepresentative value for each of the proper subsets is multiplied by thepenalty-term weight reference value acquired from the storage unit asthe penalty-term weight for each of the proper subset.
 5. The medicalimage processing device according to claim 4, wherein the penalty-termweight calculating unit sets, for each of the proper subsets, any one ofthree values which are the average value and the central value of thedetector output weight corresponding to the set elements included in theproper subset as well as the class for dividing an entire histogramcategorized by the detector output weight by a predetermined proportion,as the representative value for each of the proper subsets.
 6. Themedical image processing device according to claim 1, wherein thepenalty-term weight calculating unit determines the penalty-term weightfor each of the proper subsets so that the error between a value whichis calculated by substituting the correction amount calculating functionwhich calculates the correction amount of the projection data when thepenalty-term weight for each of the proper subsets is variable and theiterative approximation method is executed into the detector outputweight and the projection data corresponding to the set elementsincluded in the proper subset and a correction amount reference value isminimized.
 7. The medical image processing device according to claim 6,wherein the penalty-term weight calculating unit: stores, in the storagedevice in advance for each noise reducing rate, a penalty-term weightreference value which is the penalty-term weight by which the noisereducing effect comparable to that of the noise reducing rate isachieved with respect to a reference phantom; acquires, from the storagedevice, the penalty-term weight reference value corresponding to thenoise reducing rate to be input; and calculates the correction amount ofthe projection data in a case in which the detector output weight is aconstant and the iterative approximation method is executed using thepenalty-term weight reference value acquired from the storage device, soas to set the calculated correction amount as the correction amountreference value.
 8. A medical image processing method which executes aprojection data correction process and/or image reconstruction processusing an iterative approximation method in which the detector outputweight which is a weight in accordance with an output of a detector anda penalty term are included in an evaluation function, including:determining, on the basis of scanning conditions and reconstructionconditions, one or more proper subsets from the universal set of whichthe information for uniquely specifying combination of a channel of thedetector, a row of the detector and a view which is the acquisition unitof the projection data is a set element; calculating, for each of theproper subsets, a penalty-term weight which is a weight related to thepenalty term on the basis of the detector output weight corresponding tothe set elements included in the proper subset; and executing theiterative approximation method for each of the proper subsets using thepenalty-term weight.